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			<h1><font face="georgia">Matt's Webcorner</font></h1>
			<h2>Stanford 2014</h2>
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<h1>Combining Inertial Navigation and ICP for Real-time 3D Surface Reconstruction</h1>
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<a href="http://graphics.stanford.edu/~niessner/publications.html">Matthias Nießner</a>
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<span>
<a href="http://graphics.stanford.edu/">Angela Dai</a>
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<span>
<a href="http://graphics.stanford.edu/~mdfisher/publications.html">Matthew Fisher</a>
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Stanford University
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<img id="teaserD" src="papers/inertialNavigationTeaser.png"/>
<span>Eurographics 2014 Short Paper</span>
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<h2>Abstract</h2>
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<p>
We present a novel method to improve the robustness of real-time 3D surface reconstruction by incorporating
inertial sensor data when determining inter-frame alignment. With commodity inertial sensors, we can significantly
reduce the number of iterative closest point (ICP) iterations required per frame. Our system is also able to
determine when ICP tracking becomes unreliable and use inertial navigation to correctly recover tracking, even
after significant time has elapsed. This enables less experienced users to more quickly acquire 3D scans. We apply
our framework to several different surface reconstruction tasks and demonstrate that enabling inertial navigation
allows us to reconstruct scenes more quickly and recover from situations where reconstructing without IMU data
produces very poor results.
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Paper: <a href="papers/inertialNavigation.pdf"><img src="Images/pdf-icon.png"/>PDF</a>
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Video: <a href="papers/inertialNavigationVideo.mp4"><img src="Images/video-icon.png"/>MP4</a>
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Google Scholar: <a href="http://scholar.google.com/scholar?hl=en&q=Combining+Inertial+Navigation+and+ICP+for+Real-time+3D+Surface+Reconstruction&btnG=&as_sdt=1%2C5&as_sdtp="><img src="Images/google-icon.png"/></a>
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BibTeX citation:
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To be published in the Computer Graphics Forum, Eurographics 2014
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